mirror of https://github.com/open-mmlab/mmyolo.git
[Docs] Refine README (#482)
* add emoji in README * update * update * update1 * update1 * Add banner * minor fix * adjust position * add toc * update zh_CN * update zh_CN * test * update * test * test * update * update * update * update * add banner in README_zh-CN * replace emoji * minor fix * remove some videos * Update README_zh-CN.md Co-authored-by: huanghaian <huanghaian@sensetime.com>pull/477/head^2
Range King
GitHub
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README.md
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README.md
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<div align="center">
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<img src="resources/mmyolo-logo.png" width="600"/>
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<img width="100%" src="https://user-images.githubusercontent.com/27466624/213130448-1f8529fd-2247-4ac4-851c-acd0148a49b9.png"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab website</font></b>
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@ -40,38 +40,31 @@ English | [简体中文](README_zh-CN.md)
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</div>
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## Introduction
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## 📄 Table of Contents
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MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch and [MMDetection](https://github.com/open-mmlab/mmdetection). It is a part of the [OpenMMLab](https://openmmlab.com/) project.
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- [🥳 🚀 What's New](#--whats-new-)
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- [✨ Highlight](#-highlight-)
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- [📖 Introduction](#-introduction-)
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- [🛠️ Installation](#%EF%B8%8F-installation-)
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- [👨🏫 Tutorial](#-tutorial-)
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- [📊 Overview of Benchmark and Model Zoo](#-overview-of-benchmark-and-model-zoo-)
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- [❓ FAQ](#-faq-)
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- [🙌 Contributing](#-contributing-)
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- [🤝 Acknowledgement](#-acknowledgement-)
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- [🖊️ Citation](#️-citation-)
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- [🎫 License](#-license-)
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- [🏗️ Projects in OpenMMLab](#%EF%B8%8F-projects-in-openmmlab-)
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The master branch works with **PyTorch 1.6+**.
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<img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif"/>
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## 🥳 🚀 What's New [🔝](#-table-of-contents)
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<details open>
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<summary>Major features</summary>
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💎 **v0.4.0** was released on 18/1/2023:
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- **Unified and convenient benchmark**
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1. Implemented [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov8/README.md) object detection model, and supports model deployment in [projects/easydeploy](https://github.com/open-mmlab/mmyolo/blob/dev/projects/easydeploy)
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2. Added Chinese and English versions of [Algorithm principles and implementation with YOLOv8](https://github.com/open-mmlab/mmyolo/blob/dev/docs/en/algorithm_descriptions/yolov8_description.md)
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MMYOLO unifies the implementation of modules in various YOLO algorithms and provides a unified benchmark. Users can compare and analyze in a fair and convenient way.
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For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html).
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- **Rich and detailed documentation**
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MMYOLO provides rich documentation for getting started, model deployment, advanced usages, and algorithm analysis, making it easy for users at different levels to get started and make extensions quickly.
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- **Modular Design**
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MMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies.
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<img src="https://user-images.githubusercontent.com/27466624/199999337-0544a4cb-3cbd-4f3e-be26-bcd9e74db7ff.jpg" alt="BaseModule-P5"/>
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The figure above is contributed by RangeKing@GitHub, thank you very much!
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And the figure of P6 model is in [model_design.md](docs/en/algorithm_descriptions/model_design.md).
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</details>
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## What's New
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### Highlight
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### ✨ Highlight [🔝](#-table-of-contents)
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We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet).
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@ -91,16 +84,36 @@ We are excited to announce our latest work on real-time object recognition tasks
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MMYOLO currently only implements the object detection algorithm, but it has a significant training acceleration compared to the MMDeteciton version. The training speed is 2.6 times faster than the previous version.
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💎 **v0.3.0** was released on 8/1/2023:
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## 📖 Introduction [🔝](#-table-of-contents)
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1. Implement fast version of [RTMDet](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md). RTMDet-s 8xA100 training takes only 14 hours. The training speed is 2.6 times faster than the previous version.
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2. Support [PPYOLOE](https://github.com/open-mmlab/mmyolo/blob/dev/configs/ppyoloe/README.md) training
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3. Support `iscrowd` attribute training in [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov5/crowdhuman/yolov5_s-v61_8xb16-300e_ignore_crowdhuman.py)
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4. Support [YOLOv5 assigner result visualization](https://github.com/open-mmlab/mmyolo/blob/dev/projects/assigner_visualization/README.md)
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MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch and [MMDetection](https://github.com/open-mmlab/mmdetection). It is a part of the [OpenMMLab](https://openmmlab.com/) project.
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For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html).
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The master branch works with **PyTorch 1.6+**.
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<img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif"/>
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## Installation
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<details open>
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<summary>Major features</summary>
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- 🕹️ **Unified and convenient benchmark**
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MMYOLO unifies the implementation of modules in various YOLO algorithms and provides a unified benchmark. Users can compare and analyze in a fair and convenient way.
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- 📚 **Rich and detailed documentation**
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MMYOLO provides rich documentation for getting started, model deployment, advanced usages, and algorithm analysis, making it easy for users at different levels to get started and make extensions quickly.
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- 🧩 **Modular Design**
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MMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies.
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<img src="https://user-images.githubusercontent.com/27466624/199999337-0544a4cb-3cbd-4f3e-be26-bcd9e74db7ff.jpg" alt="BaseModule-P5"/>
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The figure above is contributed by RangeKing@GitHub, thank you very much!
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And the figure of P6 model is in [model_design.md](docs/en/algorithm_descriptions/model_design.md).
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</details>
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## 🛠️ Installation [🔝](#-table-of-contents)
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MMYOLO relies on PyTorch, MMCV, MMEngine, and MMDetection. Below are quick steps for installation. Please refer to the [Install Guide](docs/en/get_started.md) for more detailed instructions.
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@ -119,7 +132,7 @@ pip install -r requirements/albu.txt
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mim install -v -e .
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```
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## Tutorial
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## 👨🏫 Tutorial [🔝](#-table-of-contents)
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MMYOLO is based on MMDetection and adopts the same code structure and design approach. To get better use of this, please read [MMDetection Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the first understanding of MMDetection.
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@ -144,6 +157,8 @@ For different parts from MMDetection, we have also prepared user guides and adva
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- [Model design-related instructions](docs/en/algorithm_descriptions/model_design.md)
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- [Algorithm principles and implementation](https://mmyolo.readthedocs.io/en/latest/algorithm_descriptions/index.html#algorithm-principles-and-implementation)
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- [Algorithm principles and implementation with YOLOv5](docs/en/algorithm_descriptions/yolov5_description.md)
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- [Algorithm principles and implementation with RTMDet](docs/en/algorithm_descriptions/rtmdet_description.md)
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- [Algorithm principles and implementation with YOLOv8](docs/en/algorithm_descriptions/yolov8_description.md)
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- Deployment Guides
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@ -158,7 +173,7 @@ For different parts from MMDetection, we have also prepared user guides and adva
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- [How to](docs/en/advanced_guides/how_to.md)
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- [Plugins](docs/en/advanced_guides/plugins.md)
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## Overview of Benchmark and Model Zoo
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## 📊 Overview of Benchmark and Model Zoo [🔝](#-table-of-contents)
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Results and models are available in the [model zoo](docs/en/model_zoo.md).
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@ -205,6 +220,9 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
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<li>CSPNeXt</li>
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<li>YOLOv7Backbone</li>
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<li>PPYOLOECSPResNet</li>
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<li>mmdet backbone</li>
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<li>mmcls backbone</li>
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<li>timm</li>
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</ul>
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</td>
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<td>
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@ -221,6 +239,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
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<td>
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<ul>
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<li>IoULoss</li>
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<li>mmdet loss</li>
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</ul>
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</td>
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<td>
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@ -235,22 +254,26 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
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</details>
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## FAQ
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## ❓ FAQ [🔝](#-table-of-contents)
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Please refer to the [FAQ](docs/en/notes/faq.md) for frequently asked questions.
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## Contributing
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## 🙌 Contributing [🔝](#-table-of-contents)
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We appreciate all contributions to improving MMYOLO. Ongoing projects can be found in our [GitHub Projects](https://github.com/open-mmlab/mmyolo/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
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## Acknowledgement
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## 🤝 Acknowledgement [🔝](#-table-of-contents)
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MMYOLO is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedback.
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We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors.
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We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new detectors.
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## Citation
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<div align="center">
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<a href="https://github.com/open-mmlab/mmyolo/graphs/contributors"><img src="https://contrib.rocks/image?repo=open-mmlab/mmyolo"/></a>
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</div>
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If you find this project useful in your research, please consider cite:
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## 🖊️ Citation [🔝](#-table-of-contents)
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If you find this project useful in your research, please consider citing:
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```latex
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@misc{mmyolo2022,
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@ -261,11 +284,11 @@ If you find this project useful in your research, please consider cite:
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}
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```
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## License
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## 🎫 License [🔝](#-table-of-contents)
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This project is released under the [GPL 3.0 license](LICENSE).
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## Projects in OpenMMLab
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## 🏗️ Projects in OpenMMLab [🔝](#-table-of-contents)
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- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
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126
README_zh-CN.md
126
README_zh-CN.md
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<div align="center">
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<img src="resources/mmyolo-logo.png" width="600"/>
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<img src="https://user-images.githubusercontent.com/27466624/213156908-cef7cc50-97d1-4e0a-9e06-309bd0a49173.png" width="100%"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab 官网</font></b>
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@ -40,38 +40,52 @@
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</div>
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## 简介
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## 📄 Table of Contents
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MMYOLO 是一个基于 PyTorch 和 MMDetection 的 YOLO 系列算法开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
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- [🥳 🚀 最新进展](#--最新进展-)
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- [✨ 亮点](#-亮点-)
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- [📖 简介](#-简介-)
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- [🛠️ 安装](#️%EF%B8%8F-安装-)
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- [👨🏫 教程](#-教程-)
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- [📊 基准测试和模型库](#-基准测试和模型库-)
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- [❓ 常见问题](#-常见问题-)
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- [🙌 贡献指南](#-贡献指南-)
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- [🤝 致谢](#🤝-致谢-)
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- [🖊️ 引用](#️-引用-)
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- [🎫 开源许可证](#-开源许可证-)
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- [🏗️ OpenMMLab 的其他项目](#%EF%B8%8F-openmmlab-的其他项目-)
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- [❤️ 欢迎加入 OpenMMLab 社区](#%EF%B8%8F-欢迎加入-openmmlab-社区-)
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主分支代码目前支持 PyTorch 1.6 以上的版本。
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<img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif"/>
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## 🥳 🚀 最新进展 [🔝](#-table-of-contents)
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<details open>
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<summary>主要特性</summary>
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💎 **v0.4.0** 版本已经在 2023.1.18 发布：
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- **统一便捷的算法评测**
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1. 实现了 [YOLOv8](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov8/README.md) 目标检测模型，并通过 [projects/easydeploy](https://github.com/open-mmlab/mmyolo/blob/dev/projects/easydeploy) 支持了模型部署
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2. 新增了中英文版本的 [YOLOv8 原理和实现全解析文档](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/algorithm_descriptions/yolov8_description.md)
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MMYOLO 统一了各类 YOLO 算法模块的实现, 并提供了统一的评测流程，用户可以公平便捷地进行对比分析。
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我们提供了实用的**脚本命令速查表**
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- **丰富的入门和进阶文档**
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<div align=center>
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<img src="https://user-images.githubusercontent.com/27466624/213104312-3580c783-2423-442f-b5f6-79204a06adb5.png">
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</div>
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MMYOLO 提供了从入门到部署到进阶和算法解析等一系列文档，方便不同用户快速上手和扩展。
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你可以点击[链接](https://pan.baidu.com/s/1QEaqT7YayUdEvh1an0gjHg?pwd=yolo)，下载高清版 PDF 文件。
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- **模块化设计**
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同时我们也推出了解读视频：
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MMYOLO 将框架解耦成不同的模块组件，通过组合不同的模块和训练测试策略，用户可以便捷地构建自定义模型。
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| | 内容 | 视频 | 课程中的代码 |
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| :-: | :--------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| 🌟 | 特征图可视化 | [](https://www.bilibili.com/video/BV188411s7o8) [](https://www.bilibili.com/video/BV188411s7o8) | [特征图可视化.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/%5B%E5%B7%A5%E5%85%B7%E7%B1%BB%E7%AC%AC%E4%B8%80%E6%9C%9F%5D%E7%89%B9%E5%BE%81%E5%9B%BE%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb) |
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| 🌟 | 源码阅读和调试「必备」技巧 | [](https://www.bilibili.com/video/BV1N14y1V7mB) [](https://www.bilibili.com/video/BV1N14y1V7mB) | [源码阅读和调试「必备」技巧文档](https://zhuanlan.zhihu.com/p/580885852) |
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| 🌟 | 10分钟换遍主干网络 | [](https://www.bilibili.com/video/BV1JG4y1d7GC) [](https://www.bilibili.com/video/BV1JG4y1d7GC) | [10分钟换遍主干网络文档](https://zhuanlan.zhihu.com/p/585641598)<br>[10分钟换遍主干网络.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第二期]10分钟换遍主干网络.ipynb) |
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| 🌟 | 自定义数据集从标注到部署保姆级教程 | [](https://www.bilibili.com/video/BV1RG4y137i5) [](https://www.bilibili.com/video/BV1JG4y1d7GC) | [自定义数据集从标注到部署保姆级教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md) |
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| 🌟 | 顶会第一步 · 模块自定义 | [](https://www.bilibili.com/video/BV1yd4y1j7VD) [](https://www.bilibili.com/video/BV1yd4y1j7VD) | [顶会第一步·模块自定义.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第四期]顶会第一步·模块自定义.ipynb) |
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<img src="https://user-images.githubusercontent.com/27466624/199999337-0544a4cb-3cbd-4f3e-be26-bcd9e74db7ff.jpg" alt="基类-P5"/>
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图为 RangeKing@GitHub 提供，非常感谢！
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完整视频列表请参考 [资源汇总页面](https://mmyolo.readthedocs.io/zh_CN/latest/article.html)
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P6 模型图详见 [model_design.md](docs/zh_CN/algorithm_descriptions/model_design.md)。
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发布历史和更新细节请参考 [更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html)
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</details>
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## 最新进展
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### 亮点
|
||||
### ✨ 亮点 [🔝](#-table-of-contents)
|
||||
|
||||
我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](configs/rtmdet)找到。
|
||||
|
||||
|
|
@ -91,30 +105,36 @@ P6 模型图详见 [model_design.md](docs/zh_CN/algorithm_descriptions/model_des
|
|||
|
||||
MMYOLO 中目前仅仅实现了目标检测算法，但是相比 MMDeteciton 版本有显著训练加速，训练速度相比原先版本提升 2.6 倍。
|
||||
|
||||
💎 **v0.3.0** 版本已经在 2023.1.8 发布：
|
||||
## 📖 简介 [🔝](#-table-of-contents)
|
||||
|
||||
1. 实现了 [RTMDet](https://github.com/open-mmlab/mmyolo/blob/dev/configs/rtmdet/README.md) 的快速版本。RTMDet-s 8xA100 训练只需要 14 个小时，训练速度相比原先版本提升 2.6 倍。
|
||||
2. 支持 [PPYOLOE](https://github.com/open-mmlab/mmyolo/blob/dev/configs/ppyoloe/README.md) 训练。
|
||||
3. 支持 [YOLOv5](https://github.com/open-mmlab/mmyolo/blob/dev/configs/yolov5/crowdhuman/yolov5_s-v61_8xb16-300e_ignore_crowdhuman.py) 的 `iscrowd` 属性训练。
|
||||
4. 支持 [YOLOv5 正样本分配结果可视化](https://github.com/open-mmlab/mmyolo/blob/dev/projects/assigner_visualization/README.md)
|
||||
5. 新增 [YOLOv6 原理和实现全解析文档](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/algorithm_descriptions/yolov6_description.md)
|
||||
MMYOLO 是一个基于 PyTorch 和 MMDetection 的 YOLO 系列算法开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
|
||||
|
||||
同时我们也推出了解读视频：
|
||||
主分支代码目前支持 PyTorch 1.6 以上的版本。
|
||||
<img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif"/>
|
||||
|
||||
| | 内容 | 视频 | 课程中的代码 |
|
||||
| :-: | :--------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|
||||
| 🌟 | 特征图可视化 | [](https://www.bilibili.com/video/BV188411s7o8) [](https://www.bilibili.com/video/BV188411s7o8) | [特征图可视化.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/%5B%E5%B7%A5%E5%85%B7%E7%B1%BB%E7%AC%AC%E4%B8%80%E6%9C%9F%5D%E7%89%B9%E5%BE%81%E5%9B%BE%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb) |
|
||||
| 🌟 | 特征图可视化 Demo | [](https://www.bilibili.com/video/BV1je4y1478R/) [](https://www.bilibili.com/video/BV1je4y1478R/) | |
|
||||
| 🌟 | 配置全解读 | [](https://www.bilibili.com/video/BV1214y157ck) [](https://www.bilibili.com/video/BV1214y157ck) | [配置全解读文档](https://zhuanlan.zhihu.com/p/577715188) |
|
||||
| 🌟 | 源码阅读和调试「必备」技巧 | [](https://www.bilibili.com/video/BV1N14y1V7mB) [](https://www.bilibili.com/video/BV1N14y1V7mB) | [源码阅读和调试「必备」技巧文档](https://zhuanlan.zhihu.com/p/580885852) |
|
||||
| 🌟 | 工程文件结构简析 | [](https://www.bilibili.com/video/BV1LP4y117jS)[](https://www.bilibili.com/video/BV1LP4y117jS) | [工程文件结构简析文档](https://zhuanlan.zhihu.com/p/584807195) |
|
||||
| 🌟 | 10分钟换遍主干网络 | [](https://www.bilibili.com/video/BV1JG4y1d7GC) [](https://www.bilibili.com/video/BV1JG4y1d7GC) | [10分钟换遍主干网络文档](https://zhuanlan.zhihu.com/p/585641598)<br>[10分钟换遍主干网络.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[实用类第二期]10分钟换遍主干网络.ipynb) |
|
||||
| 🌟 | 基于 sahi 的大图推理 | [](https://www.bilibili.com/video/BV1EK411R7Ws/) [](https://www.bilibili.com/video/BV1EK411R7Ws/) | [10分钟轻松掌握大图推理.ipynb](https://github.com/open-mmlab/OpenMMLabCourse/blob/main/codes/MMYOLO_tutorials/[工具类第二期]10分钟轻松掌握大图推理.ipynb) |
|
||||
| 🌟 | 自定义数据集从标注到部署保姆级教程 | [](https://www.bilibili.com/video/BV1RG4y137i5) [](https://www.bilibili.com/video/BV1JG4y1d7GC) | [自定义数据集从标注到部署保姆级教程](https://github.com/open-mmlab/mmyolo/blob/dev/docs/zh_cn/user_guides/custom_dataset.md) |
|
||||
<details open>
|
||||
<summary>主要特性</summary>
|
||||
|
||||
发布历史和更新细节请参考 [更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html)
|
||||
- 🕹️ **统一便捷的算法评测**
|
||||
|
||||
## 安装
|
||||
MMYOLO 统一了各类 YOLO 算法模块的实现, 并提供了统一的评测流程，用户可以公平便捷地进行对比分析。
|
||||
|
||||
- 📚 **丰富的入门和进阶文档**
|
||||
|
||||
MMYOLO 提供了从入门到部署到进阶和算法解析等一系列文档，方便不同用户快速上手和扩展。
|
||||
|
||||
- 🧩 **模块化设计**
|
||||
|
||||
MMYOLO 将框架解耦成不同的模块组件，通过组合不同的模块和训练测试策略，用户可以便捷地构建自定义模型。
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/27466624/199999337-0544a4cb-3cbd-4f3e-be26-bcd9e74db7ff.jpg" alt="基类-P5"/>
|
||||
图为 RangeKing@GitHub 提供，非常感谢！
|
||||
|
||||
P6 模型图详见 [model_design.md](docs/zh_CN/algorithm_descriptions/model_design.md)。
|
||||
|
||||
</details>
|
||||
|
||||
## 🛠️ 安装 [🔝](#-table-of-contents)
|
||||
|
||||
MMYOLO 依赖 PyTorch, MMCV, MMEngine 和 MMDetection，以下是安装的简要步骤。 更详细的安装指南请参考[安装文档](docs/zh_cn/get_started.md)。
|
||||
|
||||
|
|
@ -133,7 +153,7 @@ pip install -r requirements/albu.txt
|
|||
mim install -v -e .
|
||||
```
|
||||
|
||||
## 教程
|
||||
## 👨🏫 教程 [🔝](#-table-of-contents)
|
||||
|
||||
MMYOLO 基于 MMDetection 开源库，并且采用相同的代码组织和设计方式。为了更好的使用本开源库，请先阅读 [MMDetection 概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) 对 MMDetection 进行初步地了解。
|
||||
|
||||
|
|
@ -178,7 +198,7 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
|
|||
|
||||
- [解读文章和资源汇总](docs/zh_cn/article.md)
|
||||
|
||||
## 基准测试和模型库
|
||||
## 📊 基准测试和模型库 [🔝](#-table-of-contents)
|
||||
|
||||
测试结果和模型可以在 [模型库](docs/zh_cn/model_zoo.md) 中找到。
|
||||
|
||||
|
|
@ -225,6 +245,9 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
|
|||
<li>CSPNeXt</li>
|
||||
<li>YOLOv7Backbone</li>
|
||||
<li>PPYOLOECSPResNet</li>
|
||||
<li>mmdet backbone</li>
|
||||
<li>mmcls backbone</li>
|
||||
<li>timm</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>
|
||||
|
|
@ -241,6 +264,7 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
|
|||
<td>
|
||||
<ul>
|
||||
<li>IoULoss</li>
|
||||
<li>mmdet loss</li>
|
||||
</ul>
|
||||
</td>
|
||||
<td>
|
||||
|
|
@ -255,19 +279,23 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
|
|||
|
||||
</details>
|
||||
|
||||
## 常见问题
|
||||
## ❓ 常见问题 [🔝](#-table-of-contents)
|
||||
|
||||
请参考 [FAQ](docs/zh_cn/notes/faq.md) 了解其他用户的常见问题。
|
||||
|
||||
## 贡献指南
|
||||
## 🙌 贡献指南 [🔝](#-table-of-contents)
|
||||
|
||||
我们感谢所有的贡献者为改进和提升 MMYOLO 所作出的努力。我们将正在进行中的项目添加进了[GitHub Projects](https://github.com/open-mmlab/mmyolo/projects)页面，非常欢迎社区用户能参与进这些项目中来。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
|
||||
|
||||
## 致谢
|
||||
## 🤝 致谢 [🔝](#-table-of-contents)
|
||||
|
||||
MMYOLO 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
|
||||
|
||||
## 引用
|
||||
<div align="center">
|
||||
<a href="https://github.com/open-mmlab/mmyolo/graphs/contributors"><img src="https://contrib.rocks/image?repo=open-mmlab/mmyolo"/></a>
|
||||
</div>
|
||||
|
||||
## 🖊️ 引用 [🔝](#-table-of-contents)
|
||||
|
||||
如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMYOLO
|
||||
|
||||
|
|
@ -280,11 +308,11 @@ MMYOLO 是一款由来自不同高校和企业的研发人员共同参与贡献
|
|||
}
|
||||
```
|
||||
|
||||
## 开源许可证
|
||||
## 🎫 开源许可证 [🔝](#-table-of-contents)
|
||||
|
||||
该项目采用 [GPL 3.0 开源许可证](LICENSE)。
|
||||
|
||||
## OpenMMLab 的其他项目
|
||||
## 🏗️ OpenMMLab 的其他项目 [🔝](#-table-of-contents)
|
||||
|
||||
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
|
||||
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
|
||||
|
|
@ -309,7 +337,7 @@ MMYOLO 是一款由来自不同高校和企业的研发人员共同参与贡献
|
|||
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
|
||||
- [MMEval](https://github.com/open-mmlab/mmeval): OpenMMLab 机器学习算法评测库
|
||||
|
||||
## 欢迎加入 OpenMMLab 社区
|
||||
## ❤️ 欢迎加入 OpenMMLab 社区 [🔝](#-table-of-contents)
|
||||
|
||||
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue